Data Science Practice Test
Updated October 2026Data Science Marketing Attribution
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Data Science Practice Test Questions
Prepare for the Data Science exam with our free practice test modules. Each quiz covers key topics to help you pass on your first try.
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Pro Tip: Focus your study time on areas where you score lowest on our practice quizzes below. There is no single universal "Data Science" exam — data scientist is a job role, not a licensed profession, though some professionals pursue vendor certifications (e.g. from AWS, Google Cloud, Microsoft, or DASCA) which each set their own format and fees.
Sample Data Science Practice Questions
Try these questions from our free Data Science practice tests. The correct answer and an explanation follow each question.
The task of creating a model from a reporting data warehouse is given to a data scientist. The warehouse houses data that has been processed through a difficult, multi-stage ETL process using data gathered from numerous sources. What should the data scientist be worried about in terms of the data?
- A. It is not structured
- B. It is too centralized
- C. It is too processed
- D. It is not normalized
Answer: C. It is too processed
Data in a reporting data warehouse, especially after a complex multi-stage ETL process, is often highly aggregated, transformed, and summarized for specific reporting needs. While beneficial for reporting, this extensive processing can remove granular details, introduce biases, or obscure relationships crucial for building robust predictive models. Data scientists often prefer less processed data to ensure model accuracy and flexibility.
A data scientist needs to create a visualization to compare the distribution of salaries for data analysts, data scientists, and machine learning engineers. The visualization must clearly show the median, interquartile range (IQR), and potential outliers for each job title. Which type of chart is most suitable for this purpose?
- A. A series of pie charts, one for each job title.
- B. A stacked bar chart showing the salary ranges.
- C. A box plot with separate boxes for each job title.
- D. A line chart plotting the average salary over the last five years.
Answer: C. A box plot with separate boxes for each job title.
A box plot is specifically designed to summarize the distribution of a numerical dataset. It visually represents the five-number summary: minimum, first quartile (Q1), median (Q2), third quartile (Q3), and maximum. This makes it ideal for comparing the median, interquartile range (Q3-Q1), and identifying outliers across different categories like job titles. [2, 6]
A data analyst has a dataset where each row represents a student, and columns represent their scores in different subjects: `StudentID`, `Math_Score`, `Science_Score`, `History_Score`. For a specific analysis, the data needs to be restructured so that each row contains a student ID, a subject, and the corresponding score. Which data wrangling operation should be performed?
- A. Melting
- B. Aggregating
- C. Pivoting
- D. Merging
Answer: A. Melting
Melting is the process of transforming a dataset from a wide format to a long format. [9, 13] In this case, the multiple subject score columns are "melted" into two new columns: one for the subject name ('variable') and one for the score ('value'), making the data tidy for certain plotting and analysis tasks. [4, 22] Pivoting is the reverse operation.
When cleaning a customer database, a data analyst discovers that the 'State' column contains inconsistencies such as 'CA', 'Calif.', and 'California'. What is the most appropriate data cleaning step to address this issue?
- A. Impute missing values using the mode.
- B. Remove the 'State' column from the dataset.
- C. Standardize the categorical values to a single format.
- D. Apply feature scaling to the 'State' column.
Answer: C. Standardize the categorical values to a single format.
The issue described is one of inconsistent formatting for a categorical feature. The correct approach is to standardize these values into a single, consistent format (e.g., converting all variations to 'CA'). This ensures that records are grouped correctly during analysis and that the feature is treated as a single category by machine learning models. Imputation is for missing data, removing the column would cause information loss, and feature scaling applies to numerical data.
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